Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach

The communication bottleneck of over-the-air federated learning (OA-FL) lies in aggregating the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradient aggregation by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. Based on the analysis results, we show that the optimal number of multiplexed data streams to minimize the upper bound on the FL learning performance loss is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem for the design of precoding and post-processing matrices to minimize the gradient aggregation error. To solve this problem, we develop an efficient algorithm based on alternating optimization (AO) and Karush-Kuhn-Tucker (KKT) conditions, which effectively mitigates the impact of the gradient aggregation error. Numerical results demonstrate the superb performance of the proposed SCoM approach.